This paper presents a strategy, Weighted Voting of Discriminative Regions (WVDR), to improve the face recognition performance, especially in Small Sample Size (SSS) and occlusion situations. In WVDR, we extract the discriminative regions according to facial key points and abandon the rest parts. Considering different regions of face make different contributions to recognition, we assign weights...

We establish a mistake bound for an ensemble method for classification based on maximizing the entropy of voting weights subject to margin constraints. The bound is the same as a general bound proved for the Weighted Majority Algorithm, and similar to bounds for other variants of Winnow. We prove a more refined bound that leads to a nearly optimal algorithm for learning disjunctions, again, bas...

This study presents a novel version of the Visualization Induced Self-Organizing Map based on the application of a new fusion algorithm for summarizing the results of an ensemble of topology preserving mapping models. This algorithm is referred to as Weighted Voting Superposition (WeVoS). Its main feature is the preservation of the topology of the map, in order to obtain the most truthful visua...

We study the construction of prediction algorithms in a situation in which a learner faces a sequence of trials with a prediction to be made in each and the goal of the learner is to make few mistakes We are interested in the case that the learner has reason to believe that one of some pool of known algorithms will perform well but the learner does not know which one A simple and e ective metho...

It is widely recognized that the boosting methodology provides superior results for classification problems. In this paper, we propose the boost-clustering algorithm which constitutes a novel clustering methodology that exploits the general principles of boosting in order to provide a consistent partitioning of a dataset. The boost-clustering algorithm is a multi-clustering method. At each boos...

Wireless sensor networks (WSNs) consist of hundreds or thousands of tiny sensor nodes that work together to do some special tasks. Detecting node with faulty readings is one of the important issues in WSNs. The weighted voting is usually used to detect faulty readings. Most of the existing algorithms use correlation of two nodes read vectors as weight to detect faulty readings, which result in ...

We show that n-way plurality voting on a large wlOrdered object space has time and space complexities of B(n2) and B(n), respectively. If the object space is ordered, then sorting can be used to reduce the time complexity to the optimal B(nlogn). We then prove that weighted t-out-of-Ev; threshold voting on such an object space has time complexity O(np) and needs working storage space for only p...